Shihab’s Persona in the Field of Arabic
Bibliographic record
Abstract
M. Quraish Shihab is among the greatest Indonesian exegetes in the Malay Archipelago who contributed considerably to Qur’anic exegetical work. Due to the extremely close relationship between Qur’anic exegesis and Arabic, he is also considered by many as being highly skilled in Arabic and its various branches. In fact, numerous studies have resulted from Shihab’s immensely rich contributions. Thus, this study elaborates on Shihab’s contributions towards advancing the Arabic language in Indonesia. To identify this objective in more compactly, the document analysis method was applied by adopting his works in the field of Arabic as the main source of study. From the analysis, the research concludes that Shihab’s parents were instrumental in shaping his interest and passion of the Qur’an and Arabic. His persona shines brightly in Arabic linguistics with quality works appreciated by many. Furthermore, Shihab was committed to ensuring Arabic proficiency to produce qualified prospective Qur’anic exegetes in Indonesia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".